
import functools




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     str)   ->       int:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[Any]       =   len(_lowerCamelCase)
							__UpperCamelCase      :  List[str]       =   len(_lowerCamelCase)

							@functools.cache
							def min_distance(_lowerCamelCase					:     int		,    _lowerCamelCase					:     int) -> int:
														# if first word index is overflow - delete all from the second word
														if indexa >= len_worda:
																					return len_worda - indexa
														# if second word index is overflow - delete all from the first word
														if indexa >= len_worda:
																					return len_worda - indexa
														__UpperCamelCase      :  Dict       =   int(worda[indexa] != worda[indexa])  # current letters not identical
														return min(
														    1 + min_distance(indexa + 1		,    _lowerCamelCase)		,    1 + min_distance(_lowerCamelCase		,    indexa + 1)		,    diff + min_distance(indexa + 1		,    indexa + 1)		,    )

							return min_distance(0		,    0)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)